BIAS REDUCTION OF MAXIMUM-LIKELIHOOD-ESTIMATES
BIAS REDUCTION OF MAXIMUM-LIKELIHOOD-ESTIMATES
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DOI：
10.1093/biomet/80.1.27
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发表时间：
1993-03-01
期刊：
影响因子：
2.7
通讯作者：
FIRTH, D
中科院分区：
文献类型：
--
作者：
FIRTH, D
它示出了如何，在经常性的参数问题，一阶项被删除的渐近偏差的最大似然估计的评分函数的适当修改。在具有典型参数化的指数族中，其效果是通过Jeffreys不变先验来惩罚似然。在二项逻辑模型、泊松对数线性模型和某些其他广义线性模型中，可以使用对数据进行迭代调整的方案在标准回归软件中施加Jeffreys先验罚函数。
It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In exponential families with canonical parameterization the effect is to penalize the likelihood by the Jeffreys invariant prior. In binomial logistic models, Poisson log linear models and certain other generalized linear models, the Jeffreys prior penalty function can be imposed in standard regression software using a scheme of iterative adjustments to the data.